Abstract

Posts on Twitter allow users to express ideas and opinions very dynamically. This high volume of data provides relevant clues about the public judgment on a specific product, event, service, etc. While traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level (very positive, low negative, and so on) and cannot exploit the intensity information. Recently, the problem of irony detection in social media has been proven to be pervasive among research enthusiasts, posing a challenge to sentiment analysis systems. Moreover, the figurative use of language has received scarce attention from the computational linguistic research point of view. This paper proposes an architecture, the Experts Model, inspired by the standard Mixture of Experts (MoE) model. The key idea here is that each expert learns different sets of features from the feature vector, which helps in better irony detection (Ironic vs. Non-ironic - SemEval-2018 subtask A) and ironic type detection (Verbal irony with vs. without polarity contrast vs. Situational irony vs. Non-irony - SemEval-2018 subtask B) from the tweet. We compared our Experts Model’s results with baseline results along with the top five performers of SemEval-2018 Task-3, Ironic detection. The experimental results show that our proposed approach deals with the ironic detection problem and stands at the top-3 results.

Abstract

The need for automated fact-checking is getting prominent with every passing day as the spread of misinformation is swelling over the ever-increasing stream of online content. We focus on fine-grained labelling of factual information in tweets to facilitate better fact-checking systems capable of providing improved justifications. In this paper, we present a token-level annotation of factual claims in tweets from Indian Twitter. To deal with the multilingual variety of the Indian diaspora, we deal with tweets in English, Bengali, Hindi, and their codemixed variants. To the best of our knowledge, this dataset is first of kind, both in terms of labelling scheme as well as data sources.

Abstract

Grading student work is critical for assessing their understanding and providing necessary feedback. However, answer grading can become monotonous for teachers. On the standard ASAG data set, our system shows substantial improvements in classification disparity of correct and incorrect answers from a reference answer compared to baseline methods. Our supervised model (1) utilizes recent advances in semantic word embeddings and (2) implements ideas from one-shot learning methods, which are proven to work with minimal. We present experimental results from a model based on different approaches and demonstrates decent performance on standard benchmark dataset.

Abstract

Greenhouse gases (GHGs) play an important role in controlling local air pollution as well as climate change. In this study, we retrieved column-averaged dry-air ( X ) mole fractions of carbon dioxide (CO 2 ), methane (CH 4 ), and carbon monoxide (CO) using a ground-based EM27/SUN Fourier transform infrared spectrometer (FTIR). The EM27/SUN spectrometers are widely in use in the COllaborative Carbon Column Observing Network (COCCON). The PROFFAST software provided by COCCON has been used to analyze the measured atmospheric solar absorption spectra. In this letter, the diurnal variation and the time series of daily averaged X CO 2 , X CH 4 , and X CO covering the period from December 2020 to May 2021 are analyzed. The maximum values of X CO 2 , X CH 4 , and X CO are observed to be 420.57 ppm, 1.93 ppm, and 170.40 ppb, respectively. Less diurnal but clear seasonal changes are observed during the study period. X CH 4 and X CO from the Sentinel-5Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI) are compared against the EM27/SUN retrievals. The correlation coefficient for the EM27/SUN retrieved X CH 4 and X CO, with the S5P/TROPOMI products, are 0.75 and 0.94, respectively.

Abstract

PERFORMANCE ANALYSIS OF 339 MLD SEWAGE TREATMENT PLANT AT AMBERPET – HYDERABAD. Veerannapet Santhosh Vishal1, Shaik Rehana2, N. Munilakshmi3, L Partha Praveen4 1- Research Scholar, International Institute of Information Technology, Gachibowli, Hyderbad, Telangana-500032. 2- 2- Assistant Professor, International Institute of Information Technology, Gachibowli, Hyderbad, Telangana - 500032. 3- 3- Assistant Professor, Department of Civil Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh – 517502. 4- 4- Research Scholar, Department of Civil Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh – 517502 * Corresponding author Email: santhoshvishal.v@research.iiit.ac.in Abstract Environmental and water resources have deteriorated due to rapid population growth, chemical industry effluents, agricultural practices, and climate changes. The primary cause of water contamination is due to sewage water pollution. Hence understanding and implementing an effective sewage treatment is necessary. Many cutting-edge techniques have been developed in recent years to increase the effectiveness of the removal of organic matter and nutrients by wastewater treatment plants (WWTP). To comprehend the effectiveness of wastewater treatment, the current study considered a sewage treatment plant (STP) located in Amberpet, Hyderabad. The capacity of the STP at Amberpet is 339 MLD (Million Litres per Day) and is evaluated by collecting 156 samples for 12 months (January 2018 – December 2018). An STP aims to minimize or remove organic debris, sediments, disease-causing organisms, and other pollutants in sewage water before disposing into the streams. The current study observed the removal efficiencies of the constituents Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD). This investigation assesses whether the effluents emitted into the river body are within the NRCD- set limitations (National River Conservative Directorate) as the treated sewage water is discharged into the Musi River (a tributary of Krishna Basin). The MSE values of different parameters like TSS, BOD, COD are 23.85, 28.54, 72.59. The RMSE values of TSS, BOD, COD are 4.88, 5.32, 8.52. The R2 values of TSS, BOD, COD are 0.95, 0.79, 0.90. Keywords: Wastewater Treatment Plant (WTTP), Biochemical Oxygen Demand, Chemical Oxygen Demand, Total suspended solids

Abstract

Background Depression is an all-too-common mood disorder that has been linked in various ways to the cognitive concept of attention. The Attention Network Test, based on the influential taxonomy of attention originally proposed by Posner, can be used to measure the efficacy of three components or networks of attention: Alerting, Orienting and Executive Control. Our focus here is to discover how depression might affect these networks. Methods Studies related to attention and depression were selected for this meta-analytic review using the online Attention Network Test (ANT) Database (Arora, Lawrence & Klein, 2020) resource. Based on the inclusion and exclusion criteria, 5 publications were analyzed with a Bayesian hierarchical model to compare participants with and without a diagnosis of depression. Results The posterior distribution of our model revealed no credible differences in the Alerting and Orienting networks but showed credible non-zero values for Executive Control. This suggests that Executive Control is less efficient in individuals suffering from depression. Limitations As literature using the ANT with depressed participants is limited, the methodological variability between studies in this meta-analysis should be considered when interpreting these results. Conclusion This meta-analysis review is the first quantitative review that aimed to address the ANT-depression literature. A consistent finding of no alerting and no orienting deficit but a deficit in the executive control suggests that some, but not all, components of attention are impaired in depression.

Abstract

Cognitive impairment, alterations in mood, emotion dysregulation are just a few of the consequences of depression. Despite depression being reported as the most common mental disorder worldwide, examining depression or risks of depression is still challenging. Emotional reactivity has been observed to predict the risk of depression, but the results have been mixed for negative emotional reactivity (NER). To better understand the emotional response conflict, we asked our participants to describe their feeling in meaningful sentences alongside reporting their reactions to the emotionally evocative words. We presented a word on the screen and asked participants to perform two tasks, rate their feeling after reading the word using the self-assessment manikin (SAM) scale, and describe their feeling using the property generation task. The emotional content was analyzed using a novel machine-learning algorithm approach. We performed these two tasks in blocks and

Abstract

Hashtag segmentation is the task of breaking a hashtag into its constituent tokens. Hashtags often encode the essence of usergenerated posts, along with information like topic and sentiment, which are useful in downstream tasks. Hashtags prioritize brevity and are written in unique ways - transliterating and mixing languages, spelling variations, creative named entities. Benchmark datasets used for the hashtag segmentation task - STAN, BOUN - are small in size and extracted from a single set of tweets. However, datasets should reflect the variations in writing styles of hashtags and also account for domain and language specificity, failing which the results will misrepresent model performance. We argue that model performance should be assessed on a wider variety of hashtags, and datasets should be carefully curated. To this end, we propose HashSet, a dataset comprising of: a) 1.9k manually annotated dataset; b) 3.3M loosely supervised dataset. HashSet dataset is sampled from a different set of tweets when compared to existing datasets and provides an alternate distribution of hashtags to build and validate hashtag segmentation models. We show that the performance of SOTA models for Hashtag Segmentation drops substantially on proposed dataset, indicating that the proposed dataset provides an alternate set of hashtags to train and assess models. Datasets and results are released publicly and can be accessed from https://github.com/prashantkodali/HashSet

Abstract

Code mixing is the linguistic phenomenon where bilingual speakers tend to switch between two or more languages in conversations. Recent work on code-mixing in computational settings has leveraged social media code mixed texts to train NLP models. For capturing the variety of code mixing in, and across corpus, Language ID (LID) tags based measures (CMI) have been proposed. Syntactical variety/patterns of code-mixing and their relationship vis-a-vis computational model’s performance is under explored. In this work, we investigate a collection of English(en)-Hindi(hi) code-mixed datasets from a syntactic lens to propose, SyMCoM, an indicator of syntactic variety in code-mixed text, with intuitive theoretical bounds. We train SoTA en-hi PoS tagger, accuracy of 93.4%, to reliably compute PoS tags on a corpus, and demonstrate the utility of SyMCoM by applying it on various syntactical categories on a collection of datasets, and compare datasets using the measure.

Abstract

In the last two decades, Information and Communication Technologies (ICTs) have played a pivotal role in empowering rural populations in India by making knowledge more accessible. Digital Green is one such ICT that employs a participatory approach with smallholder farmers to produce instructional agricultural videos that encompass content specific to them. With the help of human mediators, they disseminate these videos to farmers using projectors to improve the adoption of agricultural practices. Digital Green’s web-based data tracker (CoCo) stores the attendance and adoption logs of millions of farmers, the videos screened to them and their demographic information. In our work, we leverage this data for a period of ten years between 2010-2020 across five states in India where Digital Green is most active and use it to conduct a holistic evaluation of the ICT. First, we find disparities in the adoption rates of farmers, following which we use statistical tests to identify the different factors that lead to these disparities as well as gender-based inequalities. We find that farmers with higher adoption rates adopt videos of shorter duration and belong to smaller villages. Second, to provide assistance to farmers facing challenges, we model the adoption of practices from a video as a prediction problem and experiment with different model architectures. Our classifier achieves accuracies ranging from 79% to 90% across the five states, demonstrating its potential for assisting future ethnographic investigations. Third, we use SHAP values in conjunction with our model for explaining the impact of various network, content and demographic features on adoption. Our research finds that farmers greatly benefit from past adopters of a video from their group and village. We also discover that videos with a low content-specificity benefit some farmers more than others. Next, we highlight the implications of our findings by translating them into recommendations for providing focused assistance, community building, video screening, revisiting participatory approach and mitigating inequalities. Lastly, we conclude with a discussion on how our work can assist future investigations into the lived experiences of farmers. CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing. Additional Key Words and Phrases: Diagnosis, ICT4D, Agriculture, Social Networks